Apparatuses and methods for selectively inserting text into a video resume

ABSTRACT

Aspects relate to apparatuses and methods for selectively inserting text into a video resume. An exemplary apparatus includes a processor and a memory communicatively connected to the processor, the memory containing instructions configuring the processor to receive a video resume from a user, divide the video resume is into temporal sections, acquire a plurality of textual inputs from a user, wherein the plurality of textual inputs pertains to the same user of received video resume, classify the plurality of textual inputs to corresponding temporal sections of the received video resume and display, as a function of the classification, the received video resume with a corresponding plurality of textual inputs.

FIELD OF THE INVENTION

The present invention generally relates to the field of video and audiodata processing. In particular, the present invention is directed toapparatuses and methods for selectively inserting text into a videoresume.

BACKGROUND

Video technology represents a rich and effective method for conveyingdata. However, current ways of video and audio processing do notsufficiently synchronize video and textual content.

SUMMARY OF THE DISCLOSURE

In an aspect, an apparatus for selectively inserting text into a videoresume is illustrated. The apparatus including at least a processor anda memory communicatively connected to the processor, the memorycontaining instructions configuring the processor to receive a videoresume from a user, divide the video resume is into temporal sections,acquire a plurality of textual inputs from a user, wherein the pluralityof textual inputs pertains to the same user of received video resume,classify the plurality of textual inputs to corresponding temporalsections of the received video resume and display, as a function of theclassification, the received video resume with a corresponding pluralityof textual inputs.

In another aspect, a method for selectively inserting text into a videoresume is illustrated. The method includes using a computing deviceconfigured to receive a video resume from a user, divide the videoresume is into temporal sections, acquire a plurality of textual inputsfrom a user, wherein the plurality of textual inputs pertains to thesame user of received video resume, classify the plurality of textualinputs to corresponding temporal sections of the received video resumeand display, as a function of the classification, the received videoresume with a corresponding plurality of textual inputs.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram of an embodiment of an apparatus forselectively inserting text into a video resume;

FIG. 2 is a block diagram of exemplary embodiment of a machine learningmodule;

FIG. 3 illustrates an exemplary nodal network;

FIG. 4 is a block diagram of an exemplary node;

FIG. 5 is a graph illustrating an exemplary relationship between fuzzysets;

FIG. 6 is a flow diagram of an exemplary method for selectivelyinserting text into a video resume; and

FIG. 7 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed toapparatus and methods for using selectively inserting text into a videoresume.

Aspects of the present disclosure can be used to automatically analyzeand sort a user's textual data according to their resume video. Aspectsof the present disclosure can also be used to add appeal to a jobapplication.

Aspects of the present disclosure allow for practical improvement overcurrent state of art for applicant sorting by allowing for textual datato be inserted into a video resume. Exemplary embodiments illustratingaspects of the present disclosure are described below in the context ofseveral specific examples.

Referring now to FIG. 1 , an exemplary embodiment of an apparatus 100for selectively inserting text into a video resume is illustrated.Apparatus 100 includes a processor 104 and a memory 108 communicativelyconnected to processor 104, wherein memory 108 contains instructionsconfiguring processor 104 to carry out the process. Processor 104 andmemory 108 are contained in a computing device 112. As used in thisdisclosure, “communicatively connected” means connected by way of aconnection, attachment, or linkage between two or more relata whichallows for reception and/or transmittance of information therebetween.For example, and without limitation, this connection may be wired orwireless, direct, or indirect, and between two or more components,circuits, devices, systems, and the like, which allows for receptionand/or transmittance of data and/or signal(s) therebetween. Data and/orsignals therebetween may include, without limitation, electrical,electromagnetic, magnetic, video, audio, radio, and microwave dataand/or signals, combinations thereof, and the like, among others. Acommunicative connection may be achieved, for example and withoutlimitation, through wired or wireless electronic, digital, or analog,communication, either directly or by way of one or more interveningdevices or components. Further, communicative connection may includeelectrically coupling or connecting at least an output of one device,component, or circuit to at least an input of another device, component,or circuit. For example, and without limitation, via a bus or otherfacility for intercommunication between elements of a computing device.Communicative connecting may also include indirect connections via, forexample and without limitation, wireless connection, radiocommunication, low power wide area network, optical communication,magnetic, capacitive, or optical coupling, and the like. In someinstances, the terminology “communicatively coupled” may be used inplace of communicatively connected in this disclosure. A computingdevice 112 may include any computing device as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Computing device 112 may include,be included in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Computing device 112 may include a singlecomputing device operating independently, or may include two or morecomputing device operating in concert, in parallel, sequentially or thelike; two or more computing devices may be included together in a singlecomputing device or in two or more computing devices. Computing device112 may interface or communicate with one or more additional devices asdescribed below in further detail via a network interface device.Network interface device may be utilized for connecting computing device112 to one or more of a variety of networks, and one or more devices.Examples of a network interface device include, but are not limited to,a network interface card (e.g., a mobile network interface card, a LANcard), a modem, and any combination thereof. Examples of a networkinclude, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device.Computing device 112 may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. Computing device 112 may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. Computing device 112 may distribute one or morecomputing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. Computing device 112 may beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofapparatus 100 and/or computing device 112.

With continued reference to FIG. 1 , processor 104 and/or computingdevice 112 may be designed and/or configured by memory 108 to performany method, method step, or sequence of method steps in any embodimentdescribed in this disclosure, in any order and with any degree ofrepetition. For instance, processor 104 and/or computing device 112 maybe configured to perform a single step or sequence repeatedly until adesired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Processor 104 and/orcomputing device 112 may perform any step or sequence of steps asdescribed in this disclosure in parallel, such as simultaneously and/orsubstantially simultaneously performing a step two or more times usingtwo or more parallel threads, processor cores, or the like; division oftasks between parallel threads and/or processes may be performedaccording to any protocol suitable for division of tasks betweeniterations. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various ways in which steps, sequencesof steps, processing tasks, and/or data may be subdivided, shared, orotherwise dealt with using iteration, recursion, and/or parallelprocessing.

With continued reference to FIG. 1 , processor 104 and/or computingdevice 112 may perform determinations, classification, and/or analysissteps, methods, processes, or the like as described in this disclosureusing machine learning processes 116. A “machine learning process,” asused in this disclosure, is a process that automatedly uses a body ofdata known as “training data” and/or a “training set” (described furtherbelow) to generate an algorithm that will be performed by a computingdevice/module to produce outputs given data provided as inputs; this isin contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language. Machine-learning process 116 may utilizesupervised, unsupervised, lazy-learning processes and/or neuralnetworks, described further below.

Still referring to FIG. 1 , processor 104 and/or computing device 112 isconfigured to receive a video resume 120 from a user. In someembodiments, video resume 120 may be digitally compressed. In someembodiments, processor 104 and/or computing device 112 may digitallycompress video resume 120. As used in this disclosure, a “video resume”is an item of digital media in visual and/or audio form that promotesand/or describes a user. As a non-limiting example, a video resume maypromote and/or describe the user's qualifications and/or certifications.As used in this disclosure, a “user” is a person; for example, ajobseeker. In some cases, video resume 120 may include content that isrepresentative or communicative of at least an attribute of the user.Attributes may include the user's skills, competencies, experience,credentials, talents, and the like. In some cases, attributes may beexplicitly conveyed within video resume 120. Alternatively, oradditionally, in some cases, attributes may be conveyed implicitlywithin video resume 120. The user may be represented directly by videoresume 120. For example, in some cases, an image component may representa visual of the user. As used in this disclosure, an “image component”may be a visual representation of information, such as a plurality oftemporally sequential frames and/or pictures, related to a video resume120. For example, an image component may include animations, stillimagery, recorded video, and the like. In some embodiments video resume120 may be an uploaded to a user database 124 by a computing deviceoperated by a user that is communicatively connected to processor 104and/or computing device 112 through a network. A “user database” is aresource storage system used to collect and store any informationreceived from a user, such as videos, images, documents, and the like.In some embodiments, video resume 120 may be obtained by prompting auser with questions to describe their attributes.

In some cases, video resume 120 may include a digital video, which maybe communicated by way of digital signals, for example between computingdevices which are communicatively connected with at least a network. Tooptimize speed and/or cost of transmission of video resume 120. Videomay be compressed according to a video compression 128 coding format(i.e., codec). Exemplary video compression 128 codecs include H.26xcodecs, MPEG formats, SVT-AV1, and the like. In some cases, compressionof a digital video may be lossy, in which some information may be lostduring compression. Alternatively, or additionally, in some cases,compression of a digital video may be substantially lossless, wheresubstantially no information is lost during compression.

Still referring to FIG. 1 , Video resume 120 may compressed usinginter-frame coding. The “inter” part of the term refers to the use ofinter frame prediction. This kind of prediction tries to take advantagefrom temporal redundancy between neighboring frames enabling highercompression rates. Video data compression is the process of encodinginformation using fewer bits than the original representation. Anycompression is either lossy or lossless. Lossless compression reducesbits by identifying and eliminating statistical redundancy. Noinformation is lost in lossless compression. Lossy compression reducesbits by removing unnecessary or less important information. Typically, adevice that performs data compression is referred to as an encoder, andone that performs the reversal of the process (decompression) as adecoder. Compression is useful because it reduces the resources requiredto store and transmit data. Computational resources are consumed in thecompression and decompression processes. Data compression is subject toa space-time complexity trade-off. For instance, a compression schemefor video may require expensive hardware for the video to bedecompressed fast enough to be viewed as it is being decompressed, andthe option to decompress the video in full before watching it may beinconvenient or require additional storage. Video data may berepresented as a series of still image frames. Such data usuallycontains abundant amounts of spatial and temporal redundancy. Videocompression 128 algorithms attempt to reduce redundancy and storeinformation more compactly.

Still referring to FIG. 1 , inter-frame coding works by comparing eachframe in the video with the previous one. Individual frames of a videosequence are compared from one frame to the next, and the videocompression 128 codec sends only the differences to the reference frame.If the frame contains areas where nothing has moved, the system cansimply issue a short command that copies that part of the previous frameinto the next one. If sections of the frame move in a simple manner, thecompressor can emit a (slightly longer) command that tells thedecompressor to shift, rotate, lighten, or darken the copy. Usually, theencoder will also transmit a residue signal which describes theremaining more subtle differences to the reference imagery. Usingentropy coding, these residue signals have a more compact representationthan the full signal. In areas of video with more motion, thecompression must encode more data to keep up with the larger number ofpixels that are changing. As used in this disclosure, reference framesare frames of a compressed video (a complete picture) that are used todefine future frames. As such, they are only used in inter-framecompression techniques. Some modern video encoding standards, such asH.264/AVC, allow the use of multiple reference frames. This allows thevideo encoder to choose among more than one previously decoded frame onwhich to base each macroblock in the next frame. While the best framefor this purpose is usually the previous frame, the extra referenceframes can improve compression efficiency and/or video quality. The twoframe types used in inter-fame coding is P-frames and B-frames. AP-frame (Predicted picture) holds only the changes in the image from theprevious frame. For example, in a scene where a car moves across astationary background, only the car's movements need to be encoded. Theencoder does not need to store the unchanging background pixels in theP-frame, thus saving space. A B-frame (Bidirectional predicted picture)saves even more space by using differences between the current frame andboth the preceding and following frames to specify its content. An intercoded frame is divided into blocks known as macroblocks. A macroblock isa processing unit in image and video compression 128 formats based onlinear block transforms, typically the discrete cosine transform (DCT).A macroblock typically consists of 16×16 samples, and is furthersubdivided into transform blocks, and may be further subdivided intoprediction blocks. Formats which are based on macroblocks include JPEG,where they are called MCU blocks, H.261, MPEG-1 Part 2, H.262/MPEG-2Part 2, H.263, MPEG-4 Part 2, and H.264/MPEG-4 AVC. After the intercoded frame is divided into macroblocks, instead of directly encodingthe raw pixel values for each block, the encoder will try to find ablock similar to the one it is encoding on a previously encoded frame,referred to as a reference frame. This process is done by a blockmatching algorithm. If the encoder succeeds on its search, the blockcould be encoded by a vector, known as motion vector, which points tothe position of the matching block at the reference frame. The processof motion vector determination is called motion estimation. In mostcases the encoder will succeed, but the block found is likely not anexact match to the block it is encoding. This is why the encoder willcompute the differences between them. Those residual values are known asthe prediction error and need to be transformed and sent to the decoder.To sum up, if the encoder succeeds in finding a matching block on areference frame, it will obtain a motion vector pointing to the matchedblock and a prediction error. Using both elements, the decoder will beable to recover the raw pixels of the block. For example, audiovisualdatum video file may be compressed using a P-frame algorithm and brokendown into macroblocks. Individual still images taken from video resume120 can then be compared against a reference frame taken from the videofile. A P-frame from video resume 120 would only hold the changes inimage from video file. Exemplary video compression 128 codecs includewithout limitation H.26x codecs, MPEG formats. SVT-AV1, and the like. Insome cases, compression may be lossy, in which some information may belost during compression. Alternatively, or additionally, in some cases,compression may be substantially lossless, where substantially noinformation is lost during compression. In some cases, an imagecomponent may include a plurality of temporally sequential frames. Insome cases, each frame may be encoded (e.g., bitmap or vector-basedencoding). Each frame may be configured to be displayed by way of adisplay. Exemplary displays include without limitation light emittingdiode (LED) displays, cathode ray tube (CRT) displays, liquid crystaldisplays (LCDs), organic LEDs (OLDs), quantum dot displays, projectors(e.g., scanned light projectors), and the like.

Still referring to FIG. 1 , video resume 120 may be representative ofsubject-specific data. As used in this disclosure, “subject-specificdata” is any element of information that is associated with a specificform of information. Exemplary forms of subject-specific data include animage component, video resume 120, non-verbal content, verbal content,audio component, as well as any information derived directly orindirectly from video resume 120 or any other subject-specific data. Forexample, subject-specific data could be the physical properties of auser, such as their body posture or facial expression. Subject-specificdata could also be audio sensory properties of a user, such as tone ofvoice or background audio in a video resume 120. More in-depth examplesand descriptions of subject-specific data are illustrated in U.S. patentapplication Ser. No. 17/582,070, filed on Jan. 24, 2022, and entitled“SYSTEMS AND METHODS FOR PARSING AND COMPARING VIDEO RECORDDUPLICATIONS”, the entirety of which is incorporated by reference inthis disclosure.

With continued reference to FIG. 1 , in some embodiments, an imagecomponent may include or otherwise represent verbal content. Forinstance, written or visual verbal content may be included within animage component. Visual verbal content may include images of writtentext represented by an image component. For example, visual verbalcontent may include, without limitation, digitally generated graphics,images of written text (e.g., typewritten, and the like), signage, andthe like.

Still referring to FIG. 1 , in some embodiments, an image component mayinclude or otherwise represent audible verbal content related to atleast an attribute of a user. As used in this disclosure, “audibleverbal content” is oral (e.g., spoken) verbal content. In some cases,audible verbal content may be included within video resume 120 by way ofan audio component. As used in this disclosure, an “audio component” isa representation of audio, for example a sound, a speech, and the like.In some cases, verbal content may be related to at least an attribute ofsubject. Additionally, or alternatively, visual verbal content andaudible verbal content may be used as inputs to classifiers as describedthroughout this disclosure.

In some cases, processor 104 and/or computing device 112 may includeaudiovisual speech recognition (AVSR) processes to recognize verbalcontent in video resume 120. For example, processor 104 and/or computingdevice 112 may use image content to aid in recognition of audible verbalcontent such as viewing a user move their lips to speak on video toprocess the audio content of video resume 120. AVSR may use an imagecomponent to aid the overall translation of the audio verbal content ofvideo resume 120. In some embodiments, AVSR may include techniquesemploying image processing capabilities in lip reading to aid speechrecognition processes. In some cases, AVSR may be used to decode (i.e.,recognize) indeterministic phonemes or help in forming a preponderanceamong probabilistic candidates. In some cases, AVSR may include anaudio-based automatic speech recognition process and an image-basedautomatic speech recognition process. AVSR may combine results from bothprocesses with feature fusion. Audio-based speech recognition processmay analysis audio according to any method described herein, forinstance using a Mel frequency cepstrum coefficients (MFCCs) and/orlog-Mel spectrogram derived from raw audio samples. Image-based speechrecognition may perform feature recognition to yield an image vector. Insome cases, feature recognition may include any feature recognitionprocess described in this disclosure, for example a variant of aconvolutional neural network. In some cases, AVSR employs both an audiodatum and an image datum to recognize verbal content. For instance,audio vector and image vector may each be concatenated and used topredict speech made by a user, who is ‘on camera.’ Other applicablemethods of acquiring verbal content are illustrated in U.S. patentapplication Ser. No. 17/582,070, filed on Jan. 24, 2022, and entitled“SYSTEMS AND METHODS FOR PARSING AND COMPARING VIDEO RECORDDUPLICATIONS”, the entirety of which is incorporated by reference inthis disclosure.

In some cases, processor 104 and/or computing device 112 may beconfigured to recognize at least a keyword as a function of visualverbal content. In some cases, recognizing at least keyword may includeoptical character recognition. As used in this disclosure, a “keyword”is an element of word or syntax used to identify and/or match elementsto each other. In some embodiments, keywords may include interviewand/or resume prompts that a user is instructed to reply to, asdescribed further below. In some embodiments, keywords may be receivedfrom an interview prompt database, wherein the database containsguidelines, samples and example of interview prompts and responses tothe interview prompts. For example, a video resume for an accountantposition may contain interview prompts such as “work experience”,wherein a hiring entity or apparatus 100 administrator may uploadresponse samples and/or guidelines that may be used as keywords.

Still refereeing to FIG. 1 , in some embodiments, optical characterrecognition or optical character reader (OCR) includes automaticconversion of images of written (e.g., typed, handwritten or printedtext) into machine-encoded text. In some cases, recognition of at leasta keyword from an image component may include one or more processes,including without limitation optical character recognition (OCR),optical word recognition, intelligent character recognition, intelligentword recognition, and the like. In some cases, OCR may recognize writtentext, one glyph or character at a time. In some cases, optical wordrecognition may recognize written text, one word at a time, for example,for languages that use a space as a word divider. In some cases,intelligent character recognition (ICR) may recognize written text oneglyph or character at a time, for instance by employing machine-learningprocesses. In some cases, intelligent word recognition (IWR) mayrecognize written text, one word at a time, for instance by employingmachine-learning processes.

Still referring to FIG. 1 , in some cases OCR may be an “offline”process, which analyses a static document or image frame. In some cases,handwriting movement analysis can be used as input to handwritingrecognition. For example, instead of merely using shapes of glyphs andwords, this technique may capture motions, such as the order in whichsegments are drawn, the direction, and the pattern of putting the pendown and lifting it. This additional information may make handwritingrecognition more accurate. In some cases, this technology may bereferred to as “online” character recognition, dynamic characterrecognition, real-time character recognition, and intelligent characterrecognition.

Still referring to FIG. 1 , in some cases, OCR processes may employpre-processing of an image component. Pre-processing process may includewithout limitation de-skew, de-speckle, binarization, line removal,layout analysis or “zoning,” line and word detection, scriptrecognition, character isolation or “segmentation,” and normalization.In some cases, a de-skew process may include applying a transform (e.g.,homography or affine transform) to an image component to align text. Insome cases, a de-speckle process may include removing positive andnegative spots and/or smoothing edges. In some cases, a binarizationprocess may include converting an image from color or greyscale toblack-and-white (i.e., a binary image). Binarization may be performed asa simple way of separating text (or any other desired image component)from a background of image component. In some cases, binarization may berequired for example if an employed OCR algorithm only works on binaryimages. In some cases, a line removal process may include removal ofnon-glyph or non-character imagery (e.g., boxes and lines). In somecases, a layout analysis or “zoning” process may identify columns,paragraphs, captions, and the like as distinct blocks. In some cases, aline and word detection process may establish a baseline for word andcharacter shapes and separate words, if necessary. In some cases, ascript recognition process may, for example in multilingual documents,identify script allowing an appropriate OCR algorithm to be selected. Insome cases, a character isolation or “segmentation” process may separatesignal characters, for example character-based OCR algorithms. In somecases, a normalization process may normalize aspect ratio and/or scaleof image component.

Still referring to FIG. 1 , in some embodiments an OCR process mayinclude an OCR algorithm. Exemplary OCR algorithms include matrixmatching process and/or feature extraction processes. Matrix matchingmay involve comparing an image to a stored glyph on a pixel-by-pixelbasis. In some case, matrix matching may also be known as “patternmatching,” “pattern recognition,” and/or “image correlation.” Matrixmatching may rely on an input glyph being correctly isolated from therest of the image component. Matrix matching may also rely on a storedglyph being in a similar font and at a same scale as input glyph. Matrixmatching may work best with typewritten text.

Still referring to FIG. 1 , in some embodiments, an OCR process mayinclude a feature extraction process. In some cases, feature extractionmay decompose a glyph into at least a feature. Exemplary non-limitingfeatures may include corners, edges, lines, closed loops, linedirection, line intersections, and the like. In some cases, featureextraction may reduce dimensionality of representation and may make therecognition process computationally more efficient. In some cases,extracted feature may be compared with an abstract vector-likerepresentation of a character, which might reduce to one or more glyphprototypes. General techniques of feature detection in computer visionare applicable to this type of OCR. In some embodiments,machine-learning processes like nearest neighbor classifiers (e.g.,k-nearest neighbors algorithm) may be used to compare image featureswith stored glyph features and choose a nearest match. OCR may employany machine-learning process described in this disclosure, for examplemachine-learning processes described with reference to FIG. 2 .Exemplary non-limiting OCR software includes Cuneiform and Tesseract.Cuneiform is a multi-language, open-source optical character recognitionsystem originally developed by Cognitive Technologies of Moscow, Russia.Tesseract is free OCR software originally developed by Hewlett-Packardof Palo Alto, Calif., United States.

Still referring to FIG. 1 , in some cases, OCR may employ a two-passapproach to character recognition. A first pass may try to recognize acharacter. Each character that is satisfactory is passed to an adaptiveclassifier as training data. The adaptive classifier then gets a chanceto recognize characters more accurately as it further analyzes imagecomponents. Since the adaptive classifier may have learned somethinguseful a little too late to recognize characters on the first pass, asecond pass is run over the image components. Second pass may includeadaptive recognition and use characters recognized with high confidenceon the first pass to recognize better remaining characters on the secondpass. In some cases, two-pass approach may be advantageous for unusualfonts or low-quality image components where visual verbal content may bedistorted. Another exemplary OCR software tool include OCRopus. OCRopusdevelopment is led by German Research Centre for Artificial Intelligencein Kaiserslautern, Germany. In some cases, OCR software may employneural networks,

Still referring to FIG. 1 , in some cases, OCR may includepost-processing. For example, OCR accuracy may be increased, in somecases, if output is constrained by a lexicon. A lexicon may include alist or set of words that are allowed to occur in a document. In somecases, a lexicon may include, for instance, all the words in the Englishlanguage, or a more technical lexicon for a specific field. In somecases, an output stream may be a plain text stream or file ofcharacters. In some cases, an OCR process may preserve an originallayout of visual verbal content. In some cases, near-neighbor analysiscan make use of co-occurrence frequencies to correct errors, by notingthat certain words are often seen together. For example, “Washington,D.C.” is generally far more common in English than “Washington DOC.” Insome cases, an OCR process may make us of a priori knowledge of grammarfor a language being recognized. For example, grammar rules may be usedto help determine if a word is likely to be a verb or a noun. Distanceconceptualization may be employed for recognition and classification.For example, a Levenshtein distance algorithm may be used in OCRpost-processing to further optimize results.

Still referring to FIG. 1 , Processor 104 and/or computing device 112 isconfigured to divide video resume 120 into temporal sections 132. Asused herein, a “temporal section” is a clip of a video file that ismarked by a start and end time in relation to the whole video file. Aplurality of temporal sections 132 may be identified using a neuralnetwork, discussed in further detail in FIG. 3 . A neural network may betrained to output temporal sections 132 of the video file. A temporalsection 132 may be user defined such that a user may input into aprocessor, temporal sections 132 of a video resume 120. Temporal section132 may be defined in any other way is contemplated within the scope ofthis disclosure. Temporal sections 132 may be based on a resume prompt(also referred to as “prompt”) such that each section has a clip of theuser answering a prompt. As used in this disclosure, a “resume prompt”is a question, instruction, or statement used to frame the direction ofa person's response. Neural network may be trained by inputting trainingexamples of videos partitioned by hand, wherein the start of thetemporal section 132 is the prompt, and the end of the temporal section132 is the end of the user's answer to the prompt. Neural network may betrained to recognize the start of a temporal section 132 by thepresentation of a title card of the prompt and the end of a temporalsection 132 as the start of the next title card. As used herein, a“title card” is an audiovisual representation of a prompt. In anembodiment, a title card may have the prompt written on a coloredbackground before showing a user answering the prompt.

Continuing to refer to FIG. 1 , processor 104 and/or computing device112 may be configured to classify each temporal section 132 of theplurality of temporal sections 132 to a resume prompt of a plurality ofa resume prompts, wherein the plurality of a resume prompts is arrangedin a prompt ordering. As used in this disclosure, “prompt ordering” isthe arrangement in which resume prompts are introduced in video resume.For example, prompt order may be: “name”, “location”, “experience”,“education”, etc., to which the job applicant is to respond in order.

Temporal sections 132 extracted from the audiovisual datum in videoresume 120 may be classified to a resume prompt. Processor 104 and/orcomputing device 112 is configured to use audiovisual speech recognitionprocesses (AVSR) to recognize verbal content in temporal sections 132 ofthe video resume 120. Processor 104 and/or computing device 112 may useAVSR to recognize a resume prompt and associate the prompt with thetemporal section 132. In other cases, processor 104 and/or computingdevice 112 may use optical character recognition or optical characterreader (OCR) including automatic conversion of images of written (e.g.,typed, handwritten or printed text) into machine-encoded text. In thiscase, processor 104 and/or computing device 112 may recognize a titlecard and associate the title card with a prompt. Processor 104 and/orcomputing device 112 may use a classification algorithm to classifytemporal sections 132 into bins, wherein the prompt is the classifier,and the bins of data contain the temporal sections 132 related to theprompt. Additionally, the plurality of resume prompts and the associatedtemporal sections 132 may be arranged in a prompt ordering. In anembodiment, processor 104 and/or computing device 112 may take theordering of a template of prompts (discussed in an example above) andorganize temporal sections 132 into the ordering of the template.Template of prompts may be used by a classification algorithm todetermine the prompts to present to a user. In some cases, Processor 104and/or computing device 112 may organize temporal sections 132 fromgeneral prompts into more job specific prompts. For example, processor104 and/or computing device 112 may organize temporal sections 132starting from prompts about a user's background to more technicalquestions, like questions about job related tasks. Prompt ordering maybe user determined such that the user may have the option to viewtemporal sections 132 and determine the order that is presented in videoresume 120. Processor 104 and/or computing device 112 may assemble theplurality of classified temporal sections 132 into the video resume 120using the prompt ordering that may be user or machine determined.

Still referring to FIG. 1 , processor 104 and/or computing device 112 isconfigured acquire a plurality of textual inputs 136 from a user,wherein the plurality of textual inputs 136 pertains to the same user ofreceived video resume 120. As used in this disclosure, a “textual input”is subject-specific data related to the attributes of a user. Textualinput 136 may include a resume, curriculum vitae, work experience,references, cover letter, or any other materials that may be useful inthe hiring process. Input may be in the form of a word document, plaintext, pdf, and the like. Textual inputs 136 may be acquired by processor104 and/or computing device 112 in a similar manner to video resume 120,wherein the information is uploaded to user database 124 by a useroperated computing device communicatively connected to processor 104and/or computing device 112. In some embodiments, processor 104 and/orcomputing device 112 may extract textual inputs 136 from at least adocument using OCR as described above. In some embodiments, processor104 and/or computing device 112 may use a language processing module forthe extraction process. Language processing module may include anyhardware and/or software module. Language processing module may beconfigured to extract, from the one or more documents, one or morewords. One or more words may include, without limitation, strings of oneor more characters, including without limitation any sequence orsequences of letters, numbers, punctuation, diacritic marks, engineeringsymbols, geometric dimensioning and tolerancing (GD&T) symbols, chemicalsymbols and formulas, spaces, whitespace, and other symbols, includingany symbols usable as textual data as described above. Textual data maybe parsed into tokens, which may include a simple word (sequence ofletters separated by whitespace) or more generally a sequence ofcharacters as described previously. The term “token,” as used herein,refers to any smaller, individual groupings of text from a larger sourceof text; tokens may be broken up by word, pair of words, sentence, orother delimitation. These tokens may in turn be parsed in various ways.Textual data may be parsed into words or sequences of words, which maybe considered words as well. Textual data may be parsed into “n-grams”,where all sequences of n consecutive characters are considered. Any orall possible sequences of tokens or words may be stored as “chains”, forexample for use as a Markov chain or Hidden Markov Model.

Still referring to FIG. 1 , language processing module may operate toproduce a language processing model. Language processing model mayinclude a program automatically generated by Processor 104 and/orcomputing device 112 and/or language processing module to produceassociations between one or more words extracted from at least adocument and detect associations, including without limitationmathematical associations, between such words. In some embodiments,association may be based on keywords generated from video resume 120.For example, if a keyword is for a “truck driver” then any uploadedtextual inputs 136 relating to that keyword may be extracted from adocument. This may include the use of machine learning, to identify andlearn related words and/or phrases that may be associated withcredentials, skills, and traits. In some embodiments, transcribing theplurality of user inputs 132 includes using automatic speechrecognition. Associations between language elements, where languageelements include for purposes herein extracted words, relationships ofsuch categories to other such term may include, without limitation,mathematical associations, including without limitation statisticalcorrelations between any language element and any other language elementand/or language elements. Statistical correlations and/or mathematicalassociations may include probabilistic formulas or relationshipsindicating, for instance, a likelihood that a given extracted wordindicates a given category of semantic meaning. As a further example,statistical correlations and/or mathematical associations may includeprobabilistic formulas or relationships indicating a positive and/ornegative association between at least an extracted word and/or a givensemantic meaning; positive or negative indication may include anindication that a given document is or is not indicating a categorysemantic meaning. Whether a phrase, sentence, word, or other textualelement in a document or corpus of documents constitutes a positive ornegative indicator may be determined, in an embodiment, by mathematicalassociations between detected words, comparisons to phrases and/or wordsindicating positive and/or negative indicators that are stored in memoryat Processor 104 and/or computing device 112, or the like.

Still referring to FIG. 1 , language processing module and/or diagnosticengine may generate the language processing model by any suitablemethod, including without limitation a natural language processingclassification algorithm; language processing model may include anatural language process classification model that enumerates and/orderives statistical relationships between input terms and output terms.Algorithm to generate language processing model may include a stochasticgradient descent algorithm, which may include a method that iterativelyoptimizes an objective function, such as an objective functionrepresenting a statistical estimation of relationships between terms,including relationships between input terms and output terms, in theform of a sum of relationships to be estimated. In an alternative oradditional approach, sequential tokens may be modeled as chains, servingas the observations in a Hidden Markov Model (HMM). HMMs as used hereinare statistical models with inference algorithms that that may beapplied to the models. In such models, a hidden state to be estimatedmay include an association between an extracted words, phrases, and/orother semantic units. There may be a finite number of categories towhich an extracted word may pertain; an HMM inference algorithm, such asthe forward-backward algorithm or the Viterbi algorithm, may be used toestimate the most likely discrete state given a word or sequence ofwords. Language processing module may combine two or more approaches.For instance, and without limitation, machine-learning program may use acombination of Naive-Bayes (NB), Stochastic Gradient Descent (SGD), andparameter grid-searching classification techniques; the result mayinclude a classification algorithm that returns ranked associations.

Continuing to refer to FIG. 1 , generating language processing model mayinclude generating a vector space, which may be a collection of vectors,defined as a set of mathematical objects that can be added togetherunder an operation of addition following properties of associativity,commutativity, existence of an identity element, and existence of aninverse element for each vector, and can be multiplied by scalar valuesunder an operation of scalar multiplication compatible with fieldmultiplication, and that has an identity element is distributive withrespect to vector addition, and is distributive with respect to fieldaddition. Each vector in an n-dimensional vector space may berepresented by an n-tuple of numerical values. Each unique extractedword and/or language element as described above may be represented by avector of the vector space. In an embodiment, each unique extractedand/or other language element may be represented by a dimension ofvector space; as a non-limiting example, each element of a vector mayinclude a number representing an enumeration of co-occurrences of theword and/or language element represented by the vector with another wordand/or language element. Vectors may be normalized, scaled according torelative frequencies of appearance and/or file sizes. In an embodimentassociating language elements to one another as described above mayinclude computing a degree of vector similarity between a vectorrepresenting each language element and a vector representing anotherlanguage element; vector similarity may be measured according to anynorm for proximity and/or similarity of two vectors, including withoutlimitation cosine similarity, which measures the similarity of twovectors by evaluating the cosine of the angle between the vectors, whichcan be computed using a dot product of the two vectors divided by thelengths of the two vectors. Degree of similarity may include any othergeometric measure of distance between vectors.

Still referring to FIG. 1 , language processing module may use a corpusof documents to generate associations between language elements in alanguage processing module, and diagnostic engine may then use suchassociations to analyze words extracted from one or more documents anddetermine that the one or more documents indicate significance of acategory. In an embodiment, language module and/or Processor 104 and/orcomputing device 112 may perform this analysis using a selected set ofsignificant documents, such as documents identified by one or moreexperts as representing good information; experts may identify or entersuch documents via graphical user interface or may communicateidentities of significant documents according to any other suitablemethod of electronic communication, or by providing such identity toother persons who may enter such identifications into processor 104and/or computing device 112. Documents may be entered into a computingdevice by being uploaded by an expert or other persons using, withoutlimitation, file transfer protocol (FTP) or other suitable methods fortransmission and/or upload of documents; alternatively or additionally,where a document is identified by a citation, a uniform resourceidentifier (URI), uniform resource locator (URL) or other datumpermitting unambiguous identification of the document, diagnostic enginemay automatically obtain the document using such an identifier, forinstance by submitting a request to a database 124 or compendium ofdocuments such as JSTOR as provided by Ithaka Harbors, Inc. of New York.

Still referring to FIG. 1 , processor 104 and/or computing device 112 isconfigured to classify a plurality of textual inputs 136 tocorresponding temporal section 132 of the video resume 120. In someembodiments, processor 104 and/or computing device 112 may be configuredto generate a classifier 140, as described further below, to matchtextual inputs 136 to keywords and/or resume prompts related to the userof the video resume 120. For example, a keyword and/or resume promptlabeled as “Internship Experience” may be used to group the plurality oftextual inputs 136 pertaining to internship experience and categorizethe inputs with the particular keyword and/or resume prompt.Classification may include using machine learning to generate aclassifier 140. A “classifier,” as used in this disclosure is amachine-learning model, such as a mathematical model, neural net, orprogram generated by a machine learning algorithm known as a“classification algorithm,” as described in further detail below, thatsorts inputs into categories or bins of data, outputting the categoriesor bins of data and/or labels associated therewith. A classifier 140 maybe configured to output at least a datum that labels or otherwiseidentifies a set of data that are clustered together, found to be closeunder a distance metric as described below, or the like. Processor 104and/or computing device 112 and/or another device may generate aclassifier 140 using a classification algorithm, defined as a processeswhereby a processor 104 and/or computing device 112 derives a classifier140 from training data. In an embodiment, a temporal section 132classifier 140 may take, the output datum for the textual input 136classifier 140 disclosed above and output a plurality of data binscontaining textual inputs 136 matched to temporal sections 132 of thevideo resume 120. Each of the plurality of data bins may be categorizedbased on the plurality of resume prompts, keywords, temporal sections,and the like. For example, categorization may be based in resume promptssuch as name, profession, level of work experience, level of education,etc. The training data may include the plurality of temporal sections132 of video resume 120, keywords, resume prompts, user database 124,and video resume 120 verbal content. Classification may be performedusing, without limitation, linear classifiers such as without limitationlogistic regression and/or naive Bayes classifiers, nearest neighborclassifiers such as k-nearest neighbors classifiers, support vectormachines, least squares support vector machines, fisher's lineardiscriminant, quadratic classifiers, decision trees, boosted trees,random forest classifiers, learning vector quantization, and/or neuralnetwork-based classifiers.

Still referring to FIG. 1 , processor 104 and/or computing device 112may be configured to generate a classifier using a Naïve Bayesclassification algorithm. Naïve Bayes classification algorithm generatesclassifiers by assigning class labels to problem instances, representedas vectors of element values. Class labels are drawn from a finite set.Naïve Bayes classification algorithm may include generating a family ofalgorithms that assume that the value of a particular element isindependent of the value of any other element, given a class variable.Naïve Bayes classification algorithm may be based on Bayes Theoremexpressed as P(A/B)=P(B/A) P(A)÷P(B), where P(AB) is the probability ofhypothesis A given data B also known as posterior probability; P(B/A) isthe probability of data B given that the hypothesis A was true; P(A) isthe probability of hypothesis A being true regardless of data also knownas prior probability of A; and P(B) is the probability of the dataregardless of the hypothesis. A naïve Bayes algorithm may be generatedby first transforming training data into a frequency table. Processor104 and/or computing device 112 may then calculate a likelihood table bycalculating probabilities of different data entries and classificationlabels. Processor 104 and/or computing device 112 may utilize a naïveBayes equation to calculate a posterior probability for each class. Aclass containing the highest posterior probability is the outcome ofprediction. Naïve Bayes classification algorithm may include a gaussianmodel that follows a normal distribution. Naïve Bayes classificationalgorithm may include a multinomial model that is used for discretecounts. Naïve Bayes classification algorithm may include a Bernoullimodel that may be utilized when vectors are binary.

With continued reference to FIG. 1 , processor 104 and/or computingdevice 112 may be configured to generate a classifier using a K-nearestneighbors (KNN) algorithm. A “K-nearest neighbors algorithm” as used inthis disclosure, includes a classification method that utilizes featuresimilarity to analyze how closely out-of-sample-features resembletraining data to classify input data to one or more clusters and/orcategories of features as represented in training data; this may beperformed by representing both training data and input data in vectorforms, and using one or more measures of vector similarity to identifyclassifications within training data, and to determine a classificationof input data. K-nearest neighbors algorithm may include specifying aK-value, or a number directing the classifier to select the k mostsimilar entries training data to a given sample, determining the mostcommon classifier of the entries in the database 124, and classifyingthe known sample; this may be performed recursively and/or iterativelyto generate a classifier that may be used to classify input data asfurther samples. For instance, an initial set of samples may beperformed to cover an initial heuristic and/or “first guess” at anoutput and/or relationship, which may be seeded, without limitation,using expert input received according to any process as describedherein. As a non-limiting example, an initial heuristic may include aranking of associations between inputs and elements of training data.Heuristic may include selecting some number of highest-rankingassociations and/or training data elements.

With continued reference to FIG. 1 , generating k-nearest neighborsalgorithm may generate a first vector output containing a data entrycluster, generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast two values. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute 1 as derived using aPythagorean norm: 1=√Σ_(i=0){circumflex over ( )}n

a_i

{circumflex over ( )}2), where ai is attribute number i of the vector.Scaling and/or normalization may function to make vector comparisonindependent of absolute quantities of attributes, while preserving anydependency on similarity of attributes; this may, for instance, beadvantageous where cases represented in training data are represented bydifferent quantities of samples, which may result in proportionallyequivalent vectors with divergent values.

Still referring to FIG. 1 , processor 104 and/or computing device 112 isconfigured to display the received video resume 120 with a correspondingplurality of textual inputs 136. Display 144 may include using a fuzzyset inference system to determine which textual inputs 136 of theplurality of inputs should be displayed based on relevance, as disclosedfurther below. In some embodiments, processor 104 and/or computingdevice 112 may be configured to overlay video resume 120 by generating alink to an annotated copy of a plurality of relevant textual inputs 136.As used in the disclosure, an “annotated copy” of a video resume is acollection of data containing a plurality of relevant textual inputsassociated with a corresponding temporal section in a video resume. Forexample, each temporal section 132 marked in video resume 120 may have ahyperlink attached that when clicked, leads a person or an entity to therelevant plurality of textual inputs 136 pertaining to that particulartemporal section 132. For example, a temporal section 132 categorized bya keyword and/or resume prompt relating to education history, may beoverlayed with a link to a transcript of relevant textual inputs 136obtained from the same user, such as a college transcript. In someembodiments, the annotated copy may be a copy of video resume 120 withthe image component overlayed at varying temporal sections 132 with theplurality of textual inputs 136 through text, pictures, animation,video, audio, and the like. In some embodiments, the annotated copy maycontain the total plurality of textual inputs 136 with the correspondingplurality of textual inputs 136 highlighted.

Referring now to FIG. 2 , an exemplary embodiment of a machine-learningmodule 200 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes.

Still referring to FIG. 2 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 204 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 204 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 204 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 204 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 204 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 204 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data204 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 2 ,training data 204 may include one or more elements that are notcategorized; that is, training data 204 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 204 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 204 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 204 used by machine-learning module 200 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure.

Further referring to FIG. 2 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 216. Training data classifier 216 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 200 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 204. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers.

Still referring to FIG. 2 , machine-learning module 200 may beconfigured to perform a lazy-learning process 220 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 204. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 204 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 2 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 224. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 224 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 224 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 204set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 2 , machine-learning algorithms may include atleast a supervised machine-learning process 228. At least a supervisedmachine-learning process 228, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude any inputs and outputs described throughout this disclosure, anda scoring function representing a desired form of relationship to bedetected between inputs and outputs; scoring function may, for instance,seek to maximize the probability that a given input and/or combinationof elements inputs is associated with a given output to minimize theprobability that a given input is not associated with a given output.Scoring function may be expressed as a risk function representing an“expected loss” of an algorithm relating inputs to outputs, where lossis computed as an error function representing a degree to which aprediction generated by the relation is incorrect when compared to agiven input-output pair provided in training data 204. Persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of various possible variations of at least a supervisedmachine-learning process 228 that may be used to determine relationbetween inputs and outputs. Supervised machine-learning processes mayinclude classification algorithms as defined above.

Further referring to FIG. 2 , machine learning processes may include atleast an unsupervised machine-learning processes 232. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 2 , machine-learning module 200 may be designedand configured to create a machine-learning model 224 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 2 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includevarious forms of latent space regularization such as variationalregularization. Machine-learning algorithms may include Gaussianprocesses such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Referring now to FIG. 3 , an exemplary embodiment of neural network 300is illustrated. A neural network 300 also known as an artificial neuralnetwork, is a network of “nodes,” or data structures having one or moreinputs, one or more outputs, and a function determining outputs based oninputs. Such nodes may be organized in a network, such as withoutlimitation a convolutional neural network, including an input layer ofnodes, one or more intermediate layers, and an output layer of nodes.Connections between nodes may be created via the process of “training”the network, in which elements from a training dataset are applied tothe input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning. Connections may run solely from input nodes toward outputnodes in a “feed-forward” network or may feed outputs of one layer backto inputs of the same or a different layer in a “recurrent network.”

Referring now to FIG. 4 , an exemplary embodiment of a node of a neuralnetwork is illustrated. A node may include, without limitation aplurality of inputs xi that may receive numerical values from inputs toa neural network containing the node and/or from other nodes. Node mayperform a weighted sum of inputs using weights wi that are multiplied byrespective inputs xi. Additionally, or alternatively, a bias b may beadded to the weighted sum of the inputs such that an offset is added toeach unit in the neural network layer that is independent of the inputto the layer. The weighted sum may then be input into a function φ,which may generate one or more outputs y. Weight wi applied to an inputxi may indicate whether the input is “excitatory,” indicating that ithas strong influence on the one or more outputs y, for instance by thecorresponding weight having a large numerical value, and/or a“inhibitory,” indicating it has a weak effect influence on the one moreinputs y, for instance by the corresponding weight having a smallnumerical value. The values of weights wi may be determined by traininga neural network using training data, which may be performed using anysuitable process as described above.

Referring to FIG. 5 , an exemplary embodiment of fuzzy set comparison500 is illustrated. A first fuzzy set 504 may be represented, withoutlimitation, according to a first membership function 508 representing aprobability that an input falling on a first range of values 512 is amember of the first fuzzy set 504, where the first membership function508 has values on a range of probabilities such as without limitationthe interval [0,1], and an area beneath the first membership function508 may represent a set of values within first fuzzy set 504. Althoughfirst range of values 512 is illustrated for clarity in this exemplarydepiction as a range on a single number line or axis, first range ofvalues 512 may be defined on two or more dimensions, representing, forinstance, a Cartesian product between a plurality of ranges, curves,axes, spaces, dimensions, or the like. First membership function 508 mayinclude any suitable function mapping first range 512 to a probabilityinterval, including without limitation a triangular function defined bytwo linear elements such as line segments or planes that intersect at orbelow the top of the probability interval. As a non-limiting example,triangular membership function may be defined as:

${y\left( {x,a,b,c} \right)} = \left\{ \begin{matrix}{0,{{{for}x} > {c{and}x} < a}} \\{\frac{x - a}{b - a},{{{for}a} \leq x < b}} \\{\frac{c - x}{c - b},{{{if}b} < x \leq c}}\end{matrix} \right.$a trapezoidal membership function may be defined as:

${y\left( {x,a,b,c,d} \right)} = {\max\left( {{\min\left( {\frac{x - a}{b - a},1,\frac{d - x}{d - c}} \right)},0} \right)}$a sigmoidal function may be defined as:

${y\left( {x,a,c} \right)} = \frac{1}{1 - e^{- {a({x - c})}}}$a Gaussian membership function may be defined as:

${y\left( {x,c,\sigma} \right)} = e^{{- \frac{1}{2}}{(\frac{x - c}{\sigma})}^{2}}$and a bell membership function may be defined as:

${y\left( {x,a,b,c,} \right)} = \left\lbrack {1 + {❘\frac{x - c}{a}❘}^{2b}} \right\rbrack^{- 1}$Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various alternative or additionalmembership functions that may be used consistently with this disclosure.

Still referring to FIG. 5 , first fuzzy set 504 may represent any valueor combination of values as described above, including output from oneor more machine-learning models such as an acquired plurality of textualinputs. A second fuzzy set 516, which may represent any value which maybe represented by first fuzzy set 504, may be defined by a secondmembership function 520 on a second range 524; second range 524 may beidentical and/or overlap with first range 512 and/or may be combinedwith first range via Cartesian product or the like to generate a mappingpermitting evaluation overlap of first fuzzy set 504 and second fuzzyset 516. Where first fuzzy set 504 and second fuzzy set 516 have aregion 528 that overlaps, first membership function 508 and secondmembership function 520 may intersect at a point 532 representing aprobability, as defined on probability interval, of a match betweenfirst fuzzy set 504 and second fuzzy set 516. Alternatively oradditionally, a single value of first and/or second fuzzy set may belocated at a locus 536 on first range 512 and/or second range 524, wherea probability of membership may be taken by evaluation of firstmembership function 508 and/or second membership function 520 at thatrange point. A probability at 528 and/or 532 may be compared to athreshold 540 to determine whether a positive match is indicated.Threshold 540 may, in a non-limiting example, represent a degree ofmatch between first fuzzy set 504 and second fuzzy set 516, and/orsingle values therein with each other or with either set, which issufficient for purposes of the matching process; for instance, thresholdmay indicate a sufficient degree of overlap between an output from oneor more machine-learning models and/or the plurality of textual inputsand a predetermined class, such as without limitation the plurality oftemporal sections of the video resume, for combination to occur asdescribed above. Alternatively or additionally, each threshold may betuned by a machine-learning and/or statistical process, for instance andwithout limitation as described in further detail below.

Further referring to FIG. 5 , in an embodiment, a degree of matchbetween fuzzy sets may be used to classify a plurality textual inputwith the plurality of temporal sections of the video resume. Forinstance, if the plurality of textual inputs have a fuzzy set matchingthe plurality of temporal sections of the video resume fuzzy set byhaving a degree of overlap exceeding a threshold, Processor 104 and/orcomputing device 112 may classify the plurality of textual inputs asbelonging to the plurality of temporal sections of the video resume.Where multiple fuzzy matches are performed, degrees of match for eachrespective fuzzy set may be computed and aggregated through, forinstance, addition, averaging, or the like, to determine an overalldegree of match.

Still referring to FIG. 5 , in an embodiment, a plurality of textualinputs may be compared to multiple the plurality of temporal sections ofthe video resume fuzzy sets. For instance, the plurality of textualinputs may be represented by a fuzzy set that is compared to each of themultiple the plurality of temporal sections of the video resume fuzzysets; and a degree of overlap exceeding a threshold between theplurality of textual inputs fuzzy set and any of the multiple theplurality of temporal sections of the video resume fuzzy sets may causeProcessor 104 and/or computing device 112 to classify the plurality oftextual inputs as belonging to the plurality of temporal sections of thevideo resume. For instance, in one embodiment there may be two theplurality of temporal sections of the video resume fuzzy sets,representing respectively a first temporal section of the video resumeand the second temporal section of the video resume. First temporalsection of the video resume may have a first fuzzy set; second temporalsection of the video resume may have a second fuzzy set; and theplurality of textual inputs may have a textual input fuzzy set.Processor 104 and/or computing device 112, for example, may compare theplurality of textual inputs fuzzy set with each of the of the firsttemporal section of the video resume fuzzy set and the second temporalsection of the video resume fuzzy set, as described above, and classifythe plurality of textual inputs to either, both, or neither of theplurality of temporal sections of the video resume. Machine-learningmethods as described throughout may, in a non-limiting example, generatecoefficients used in fuzzy set equations as described above, such aswithout limitation x, c, and σ of a Gaussian set as described above, asoutputs of machine-learning methods. Likewise, the plurality of textualinputs may be used indirectly to determine a fuzzy set, as the pluralityof textual inputs fuzzy set may be derived from outputs of one or moremachine-learning models that take the plurality of textual inputsdirectly or indirectly as inputs.

Still referring to FIG. 5 , a computing device may use a logiccomparison program, such as, but not limited to, a fuzzy logic model todetermine a relevance score. A relevance score may include, but is notlimited to, amateur, average, knowledgeable, superior, and the like;each such relevance score may be represented as a value for a linguisticvariable representing the relevance score, or in other words a fuzzy setas described above that corresponds to a degree of compatibility ascalculated using any statistical, machine-learning, or other method thatmay occur to a person skilled in the art upon reviewing the entirety ofthis disclosure. In other words, a given element of the plurality oftextual inputs may have a first non-zero value for membership in a firstlinguistic variable value such as “compatible,” and a second non-zerovalue for membership in a second linguistic variable value such as“irrelevant” In some embodiments, determining a relevance score mayinclude using a linear regression model. A linear regression model mayinclude a machine learning model. A linear regression model may beconfigured to map data of the plurality of textual inputs, such asemployment history to one or more relevance scores. A linear regressionmodel may be trained using any training data described through thisdisclosure. In some embodiments, determining a relevance score of theplurality of textual inputs may include using a relevance scoreclassification model. A relevance score classification model may beconfigured to input collected data and cluster data to a centroid basedon, but not limited to, frequency of appearance, linguistic indicatorsof compatibility, and the like. Centroids may include scores assigned tothem such that elements of the plurality of textual inputs may each beassigned a score. In some embodiments, a relevance score classificationmodel may include a K-means clustering model. In some embodiments, arelevance score classification model may include a particle swarmoptimization model. In some embodiments, determining a relevance scoreof the plurality of textual inputs may include using a fuzzy inferenceengine. A fuzzy inference engine may be configured to map one or morethe plurality of textual inputs data elements using fuzzy logic. In someembodiments, a plurality textual inputs may be arranged by a logiccomparison program into relevance score arrangements. An “relevancescore arrangement” as used in this disclosure is any grouping of objectsand/or data based on skill level and/or output score. Membershipfunction coefficients and/or constants as described above may be tunedaccording to classification and/or clustering algorithms. For instance,and without limitation, a clustering algorithm may determine a Gaussianor other distribution of questions about a centroid corresponding to agiven compatibility level, and an iterative or other method may be usedto find a membership function, for any membership function type asdescribed above, that minimizes an average error from the statisticallydetermined distribution, such that, for instance, a triangular orGaussian membership function about a centroid representing a center ofthe distribution that most closely matches the distribution. Errorfunctions to be minimized, and/or methods of minimization, may beperformed without limitation according to any error function and/orerror function minimization process and/or method as described in thisdisclosure.

Further referring to FIG. 5 , an inference engine may be implementedaccording to input and/or output membership functions and/or linguisticvariables. For instance, a first linguistic variable may represent afirst measurable value pertaining to element of the plurality of textualinputs, such as a degree of compatibility of an element of the pluralityof textual inputs, while a second membership function may indicate adegree of relevancy of a subject thereof, or another measurable valuepertaining to the plurality of textual inputs. Continuing the example,an output linguistic variable may represent, without limitation, a scorevalue. An inference engine may combine rules, such as: “if thecompatibility is ‘high and the relevance level is ‘high’, the relevancescore to the temporal section is ‘high’”—the degree to which a giveninput function membership matches a given rule may be determined by atriangular norm or “T-norm” of the rule or output membership functionwith the input membership function, such as min (a, b), product of a andb, drastic product of a and b, Hamacher product of a and b, or the like,satisfying the rules of commutativity (T(a, b)=T(b, a)), monotonicity:(T(a, b)≤T(c, d) if a≤c and b≤d), (associativity: T(a, T(b, c))=T(T(a,b), c)), and the requirement that the number 1 acts as an identityelement. Combinations of rules (“and” or “or” combination of rulemembership determinations) may be performed using any T-conorm, asrepresented by an inverted T symbol or “⊥,” such as max(a, b),probabilistic sum of a and b (a+b−a*b), bounded sum, and/or drasticT-conorm; any T-conorm may be used that satisfies the properties ofcommutativity: ⊥(a, b)=⊥(b, a), monotonicity: ⊥(a, b)≤⊥(c, d) if a≤c andb≤d, associativity: ⊥(a, ⊥(b, c))=⊥(⊥(a, b), c), and identity element of0. Alternatively or additionally T-conorm may be approximated by sum, asin a “product-sum” inference engine in which T-norm is product andT-conorm is sum. A final output score or other fuzzy inference outputmay be determined from an output membership function as described aboveusing any suitable defuzzification process, including without limitationMean of Max defuzzification, Centroid of Area/Center of Gravitydefuzzification, Center Average defuzzification, Bisector of Areadefuzzification, or the like. Alternatively or additionally, outputrules may be replaced with functions according to the Takagi-Sugeno-King(TSK) fuzzy model.

Referring to FIG. 6 , is an exemplary overview of a method 600 forselectively inserting text into a video resume. Method 600 includesusing a computing device, being any computing device describedthroughout this disclosure, for example and with reference to FIG. 1 .At step 605, method 600 includes using a computing device to receive avideo resume from a user, wherein the video resume is digitallycompressed by the computing device. A video resume, as previouslydefined, may include content that is representative or communicative ofat least an attribute of the user, for example and with reference toFIG. 1 . In some cases, attributes may be explicitly conveyed withinvideo resume. Alternatively, or additionally, in some cases, attributesmay be conveyed implicitly within video resume. The user, as previouslydefined, may be represented directly by video resume. For example, insome cases, an image component may represent a visual of the user. Asdisclosed in FIG. 1 , an image component may include animations, stillimagery, recorded video, and the like. In some embodiments the videoresume may be an uploaded to a video resume database by a computingdevice operated by a user that is communicatively connected to themethod 600 computing device through a network. In some embodiments, thevideo resume may be obtained by prompting a user with questions todescribe their attributes. To optimize speed and/or cost of transmissionof the video resume, the video may be compressed according to a videocompression coding format (i.e., codec). Exemplary video compressioncodecs include H.26x codecs, MPEG formats, SVT-AV1, and the like. Insome cases, compression of a digital video may be lossy, in which someinformation may be lost during compression. Alternatively, oradditionally, in some cases, compression of a digital video may besubstantially lossless, where substantially no information is lostduring compression. The video resume may be compressed as describe inFIG. 1 .

Still referring to step 605, in some embodiments, an image component mayinclude or otherwise represent verbal content and audible content, forexample and with reference to FIG. 1 . In some cases, verbal content maybe related to at least an attribute of subject. Additionally, oralternatively, visual verbal content and audible verbal content may beused as inputs to classifiers as described throughout this disclosure.In some cases, the computing device may utilize audiovisual speechrecognition (AVSR) processes and optical character recognition torecognize verbal content in the video resume and convert it intomachine-coded text, for example and with reference to FIG. 1 .

Still referring to FIG. 6 , at step 610, the method 600 includes using acomputing device configured to divide the video resume is into temporalsections, as described in FIG. 1 . A plurality of temporal sections maybe identified using a neural network. A neural network may be trained tooutput temporal sections of the video file. A temporal section may beuser defined such that a user may input into a processor, temporalsections of a video resume. A temporal section may be defined in anyother way is contemplated within the scope of this disclosure. Temporalsections may be based on a resume prompt (also referred to as “prompt”)such that each section has a clip of the user answering a prompt. Neuralnetwork may be trained by inputting training examples of videospartitioned by hand, wherein the start of the temporal section is theprompt, and the end of the temporal section is the end of the user'sanswer to the prompt. Neural network may be trained to recognize thestart of a temporal section by the presentation of a title card of theprompt and the end of a temporal section as the start of the next titlecard. As used herein, a “title card” is an audiovisual representation ofa prompt. In an embodiment, a title card may have the prompt written ona colored background before showing a user answering the prompt.Additionally, the computing device may be configured to classify eachtemporal section of the plurality of temporal sections to a resumeprompt of a plurality of a resume prompt, where the plurality of aresume prompts is arranged in a prompt ordering. Temporal sectionsextracted from the audiovisual datum in video resume may be classifiedto a resume prompt, for example and with reference to FIG. 1 .

Still referring to FIG. 6 , method 600 may include using a computingdevice configured to use audiovisual speech recognition processes(AVSR), as described throughout this disclosure, to recognize verbalcontent in temporal sections of the video resume. The computing devicemay use AVSR to recognize a resume prompt and associate the prompt withthe temporal section. In other cases, the computing device may useoptical character recognition or optical character reader (OCR), asdescribed throughout this disclosure, including automatic conversion ofimages of written (e.g., typed, handwritten or printed text) intomachine-encoded text. In this case, the computing device may recognize atitle card and associate the title card with a prompt. The computingdevice may use a classification algorithm to classify temporal sectionsinto bins, wherein the prompt is the classifier, and the bins of datacontain the temporal sections related to the prompt. Additionally, theplurality of resume prompts and the associated temporal sections may bearranged in a prompt ordering. In an embodiment, the computing devicemay take the ordering of a template of prompts (discussed in an exampleabove) and organize temporal sections into the ordering of the template.Template of prompts may be used by a classification algorithm todetermine the prompts to present to a user, for example and withreference to FIGS. 1 and 2 .

At step 615, method 600 includes using a computing device configuredacquire a plurality of textual inputs from a user, wherein the pluralityof textual inputs pertains to the same user of received video resume. Aspreviously defined, textual input may include a resume, curriculumvitae, work experience, references, cover letter, or any other materialsthat may be useful in the hiring process. Input may be in the form of aword document, plain text, pdf, and the like. Textual inputs may beacquired by the computing device in a similar manner to video resume,wherein the information is uploaded by a user operated computing devicecommunicatively connected to the computing device. In some embodiments,the computing device may extract textual inputs from at least a documentusing OCR as described above. In some embodiments, the computing devicemay use a language processing model for the extraction process, forexample and with reference to FIG. 1 . Language processing module mayinclude any hardware and/or software module. Language processing modulemay be configured to extract, from the one or more documents, one ormore words. One or more words may include, without limitation, stringsof one or more characters, including without limitation any sequence orsequences of letters, numbers, punctuation, diacritic marks, engineeringsymbols, geometric dimensioning and tolerancing (GD&T) symbols, chemicalsymbols and formulas, spaces, whitespace, and other symbols, includingany symbols usable as textual data as described above. Textual data maybe parsed into tokens, which may include a simple word (sequence ofletters separated by whitespace) or more generally a sequence ofcharacters as described previously. The term “token,” as used herein,refers to any smaller, individual groupings of text from a larger sourceof text; tokens may be broken up by word, pair of words, sentence, orother delimitation. These tokens may in turn be parsed in various ways.Textual data may be parsed into words or sequences of words, which maybe considered words as well. Textual data may be parsed into “n-grams”,where all sequences of n consecutive characters are considered. Any orall possible sequences of tokens or words may be stored as “chains”, forexample for use as a Markov chain or Hidden Markov Model.

At step 620, method 600 includes using a computing device configured toclassify a plurality of textual inputs to corresponding temporalsections of the video resume. In some embodiments, the computing devicemay be configured to generate a classifier, as described throughout thisdisclosure, to match textual inputs to keywords and/or resume promptsrelated to the user of the video resume. For example, a keyword and/orresume prompt labeled as “Internship Experience” may be used to groupthe plurality of textual inputs pertaining to internship experience andcategorize the inputs with the particular keyword and/or resume prompt.Moreover, the computing device is configured to classify the pluralityof textual inputs to corresponding temporal sections of the receivedvideo resume. Classification may include using machine learning togenerate any classifier as defined and described in FIGS. 1 and 2 .

Still referring to FIG. 6 , at step 625, method 600 includes using acomputing device configured to display a video resume with acorresponding plurality of textual inputs. Displaying may include usinga fuzzy set inference system to determine which textual inputs of theplurality of inputs should be displayed based on relevance, as disclosedin FIG. 5 . In some embodiments, the computing device may be configuredto overlay video resume by generating a link to an annotated copy of aplurality of relevant textual inputs. Each temporal section marked inthe video resume may have a clickable link attached that when clicked,leads a person or an entity to the relevant plurality of textual inputspertaining to that particular temporal section. For example, a temporalsection categorized by a keyword and/or resume prompt relating toeducation history, may be overlayed with a link to a transcript ofrelevant textual inputs obtained from the same user, such as a collegetranscript.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 7 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 700 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 700 includes a processor 704 and a memory708 that communicate with each other, and with other components, via abus 712. Bus 712 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 704 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 704 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 704 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 708 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 716 (BIOS), including basic routines that help totransfer information between elements within computer system 700, suchas during start-up, may be stored in memory 708. Memory 708 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 720 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 708 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 700 may also include a storage device 724. Examples of astorage device (e.g., storage device 724) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 724 may be connected to bus 712 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 724 (or one or morecomponents thereof) may be removably interfaced with computer system 700(e.g., via an external port connector (not shown)). Particularly,storage device 724 and an associated machine-readable medium 728 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 700. In one example, software 720 may reside, completelyor partially, within machine-readable medium 728. In another example,software 720 may reside, completely or partially, within processor 704.

Computer system 700 may also include an input device 732. In oneexample, a user of computer system 700 may enter commands and/or otherinformation into computer system 700 via input device 732. Examples ofan input device 732 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 732may be interfaced to bus 712 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 712, and any combinations thereof. Input device 732 mayinclude a touch screen interface that may be a part of or separate fromdisplay 736, discussed further below. Input device 732 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 700 via storage device 724 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 740. A network interfacedevice, such as network interface device 740, may be utilized forconnecting computer system 700 to one or more of a variety of networks,such as network 744, and one or more remote devices 748 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 744,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 720,etc.) may be communicated to and/or from computer system 700 via networkinterface device 740.

Computer system 700 may further include a video display adapter 752 forcommunicating a displayable image to a display device, such as displaydevice 736. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 752 and display device 736 may be utilized incombination with processor 704 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 700 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 712 via a peripheral interface 756. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,apparatuses, and software according to the present disclosure.Accordingly, this description is meant to be taken only by way ofexample, and not to otherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. An apparatus for using machine learning to inserttext into a video resume, the apparatus comprising: at least aprocessor; and a memory communicatively connected to the processor, thememory containing instructions configuring the at least a processor to:receive a video resume from a user; divide the video resume intotemporal sections, wherein dividing the video resume comprises:receiving training data correlating video resumes to temporal sections;training a machine-learning model with the training data, wherein themachine learning model is configured to input video resumes and outputtemporal sections; and dividing, as a function of the machine learningmodel, the video resume into temporal sections, wherein the temporalsections include resume prompts; acquire a plurality of textual inputsfrom a user, wherein the plurality of textual inputs pertains to theuser of the received video resume; classify the plurality of textualinputs to corresponding temporal sections of the received video resume;and display, as a function of the classification, the received videoresume with a corresponding plurality of textual inputs.
 2. Theapparatus of claim 1, wherein: the video resume contains at least verbalcontent; and the memory contains instructions further configuring theprocessor to utilize at least an audiovisual speech recognition processto convert the verbal content into textual data.
 3. The apparatus ofclaim 2, wherein the memory contains instructions further configuringthe processor to utilize optical character recognition to convert verbalcontent into machine-encoded text.
 4. The apparatus of claim 1, whereindividing the video resume into temporal sections comprises using atleast an audiovisual speech recognition process.
 5. The apparatus ofclaim 1, wherein acquiring the plurality of textual inputs comprisesusing a language processing module to extract at least a textual inputof the plurality of textual inputs.
 6. The apparatus of claim 1, whereinthe memory contains instructions further configuring the processor torecognize keywords of verbal content, wherein the verbal content iscontained within the received video resume.
 7. The apparatus of claim 6,wherein recognizing keywords of verbal content comprises using opticalcharacter recognition.
 8. The apparatus of claim 1, wherein classifyingthe plurality of textual inputs to corresponding temporal sections ofthe received video resume comprises using a machine learning classifier.9. The apparatus of claim 1, wherein displaying the received videoresume with a corresponding plurality of textual inputs comprises usinga fuzzy set inference system to determine which textual inputs of theplurality of inputs should be displayed based on relevance.
 10. Theapparatus of claim 9, wherein the memory contains further instructionsconfiguring the processor to overlay the received video resume bygenerating a link to an annotated copy of a plurality of relevanttextual inputs.
 11. A method for selectively inserting text into a videoresume, the method comprising: receiving, using a computing device, avideo resume from a user; dividing, using the computing device, thevideo resume into temporal sections wherein dividing the video resumecomprises: receiving training data correlating video resumes to temporalsections; training a machine learning model with the training data,wherein the machine learning model is configured to input video resumesand output temporal sections; and dividing, as a function of the machinelearning model, the video resume into temporal sections, wherein thetemporal sections include resume prompts; acquiring, using the computingdevice, a plurality of textual inputs from a user, wherein the pluralityof textual inputs pertains to the same user of the received videoresume; classifying, using the computing device, the plurality oftextual inputs to corresponding temporal sections of the received videoresume; and displaying, using the computing device, as a function of theclassification, the received video resume with a corresponding pluralityof textual inputs.
 12. The method of claim 11, wherein: the video resumecontains at least verbal content; and the computing device is configuredto utilize at least an audiovisual speech recognition process to convertthe verbal content into textual data.
 13. The method of claim 12,wherein the computing device is configured to utilize optical characterrecognition to convert verbal content into machine-encoded text.
 14. Themethod of claim 11, wherein dividing the video resume into temporalsections comprises using at least an audiovisual speech recognitionprocess.
 15. The method of claim 11, wherein acquiring the plurality oftextual inputs comprises using a language processing module to extractat least a textual input of the plurality of textual inputs.
 16. Themethod of claim 11, wherein the computing device is configured torecognize keywords of verbal content, wherein the verbal content iscontained within the received video resume.
 17. The method of claim 16,wherein recognizing keywords of verbal content comprises using opticalcharacter recognition.
 18. The method of claim 11, wherein classifyingthe plurality of textual inputs to corresponding temporal sections ofthe received video resume comprises using a machine learning classifier.19. The method of claim 11, wherein displaying the received video resumewith a corresponding plurality of textual inputs comprises using a fuzzyset inference system to determine which textual inputs of the pluralityof inputs should be displayed based on relevance.
 20. The method ofclaim 19, wherein the computing device is configured to overlay thereceived video resume by generating a link to an annotated copy of aplurality of relevant textual inputs.